This project will identify the molecular mechanisms through which the gut microbiome interacts with infectious intestinal helminths. Intestinal helminthic parasites present one of the most pressing global health problems due to the abundance of infection, a limited understanding of etiology, and increasing drug resistance. Prior work has established that the gut microbiome can influence infection, but the specific mechanisms through which it does so are unknown. We hypothesize that the gut microbiome produces anti-helminthic metabolites that can prevent and control infection. We propose a series of experiments that integrate multi'omics data and machine learning techniques to discover natural products of the gut microbiome that predict infectious burden or associate with infection control. Our studies leverage a high-throughput zebrafish model of infection to precisely resolve these factors. Additionally, we will develop bioinformatic methods that (1) connect natural products to the genetic pathways and microbiota that produce them, and (2) impute the presence of these natural products in the human gut. Consequently, this project will produce novel anti-helminthic drug leads that can be efficiently isolated and tested, and will clarify the role of gut microbiome natural products in the etiology of infection.